Overview

Dataset statistics

Number of variables34
Number of observations1941
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory515.7 KiB
Average record size in memory272.1 B

Variable types

Numeric24
Categorical10

Alerts

X_Minimum is highly overall correlated with X_Maximum and 1 other fieldsHigh correlation
X_Maximum is highly overall correlated with X_Minimum and 1 other fieldsHigh correlation
Y_Minimum is highly overall correlated with Y_MaximumHigh correlation
Y_Maximum is highly overall correlated with Y_MinimumHigh correlation
Pixels_Areas is highly overall correlated with X_Perimeter and 10 other fieldsHigh correlation
X_Perimeter is highly overall correlated with Pixels_Areas and 10 other fieldsHigh correlation
Y_Perimeter is highly overall correlated with Pixels_Areas and 10 other fieldsHigh correlation
Sum_of_Luminosity is highly overall correlated with Pixels_Areas and 9 other fieldsHigh correlation
Minimum_of_Luminosity is highly overall correlated with Pixels_Areas and 6 other fieldsHigh correlation
Maximum_of_Luminosity is highly overall correlated with Luminosity_IndexHigh correlation
Length_of_Conveyer is highly overall correlated with K_ScatchHigh correlation
Steel_Plate_Thickness is highly overall correlated with TypeOfSteel_A300 and 2 other fieldsHigh correlation
Empty_Index is highly overall correlated with X_Perimeter and 3 other fieldsHigh correlation
Outside_X_Index is highly overall correlated with Pixels_Areas and 10 other fieldsHigh correlation
Edges_X_Index is highly overall correlated with Pixels_Areas and 7 other fieldsHigh correlation
Edges_Y_Index is highly overall correlated with Pixels_Areas and 10 other fieldsHigh correlation
LogOfAreas is highly overall correlated with Pixels_Areas and 12 other fieldsHigh correlation
Log_X_Index is highly overall correlated with Pixels_Areas and 11 other fieldsHigh correlation
Log_Y_Index is highly overall correlated with Pixels_Areas and 11 other fieldsHigh correlation
Orientation_Index is highly overall correlated with Edges_X_Index and 4 other fieldsHigh correlation
Luminosity_Index is highly overall correlated with Minimum_of_Luminosity and 1 other fieldsHigh correlation
SigmoidOfAreas is highly overall correlated with Pixels_Areas and 11 other fieldsHigh correlation
TypeOfSteel_A300 is highly overall correlated with Steel_Plate_Thickness and 1 other fieldsHigh correlation
TypeOfSteel_A400 is highly overall correlated with Steel_Plate_Thickness and 1 other fieldsHigh correlation
Outside_Global_Index is highly overall correlated with Orientation_IndexHigh correlation
K_Scatch is highly overall correlated with X_Minimum and 11 other fieldsHigh correlation
Stains is highly overall correlated with LogOfAreas and 1 other fieldsHigh correlation
Pastry is highly imbalanced (59.3%)Imbalance
Z_Scratch is highly imbalanced (53.8%)Imbalance
Stains is highly imbalanced (77.1%)Imbalance
Dirtiness is highly imbalanced (81.4%)Imbalance
X_Perimeter is highly skewed (γ1 = 21.5394512)Skewed
Y_Perimeter is highly skewed (γ1 = 39.29315841)Skewed
X_Minimum has 38 (2.0%) zerosZeros
Edges_Index has 38 (2.0%) zerosZeros
Orientation_Index has 91 (4.7%) zerosZeros

Reproduction

Analysis started2023-05-18 11:06:53.660875
Analysis finished2023-05-18 11:08:15.077681
Duration1 minute and 21.42 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

X_Minimum
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct962
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean571.13601
Minimum0
Maximum1705
Zeros38
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:15.155683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q151
median435
Q31053
95-th percentile1538
Maximum1705
Range1705
Interquartile range (IQR)1002

Descriptive statistics

Standard deviation520.69067
Coefficient of variation (CV)0.91167543
Kurtosis-1.1451435
Mean571.13601
Median Absolute Deviation (MAD)395
Skewness0.5008972
Sum1108575
Variance271118.78
MonotonicityNot monotonic
2023-05-18T20:08:15.300852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 126
 
6.5%
39 125
 
6.4%
0 38
 
2.0%
43 18
 
0.9%
37 12
 
0.6%
2 8
 
0.4%
19 8
 
0.4%
9 8
 
0.4%
13 7
 
0.4%
15 7
 
0.4%
Other values (952) 1584
81.6%
ValueCountFrequency (%)
0 38
2.0%
1 6
 
0.3%
2 8
 
0.4%
3 3
 
0.2%
4 4
 
0.2%
5 3
 
0.2%
6 4
 
0.2%
7 3
 
0.2%
8 3
 
0.2%
9 8
 
0.4%
ValueCountFrequency (%)
1705 1
 
0.1%
1688 1
 
0.1%
1687 2
0.1%
1685 1
 
0.1%
1683 1
 
0.1%
1682 1
 
0.1%
1680 1
 
0.1%
1678 1
 
0.1%
1677 3
0.2%
1675 1
 
0.1%

X_Maximum
Real number (ℝ)

Distinct994
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean617.96445
Minimum4
Maximum1713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:15.450853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile29
Q1192
median467
Q31072
95-th percentile1561
Maximum1713
Range1709
Interquartile range (IQR)880

Descriptive statistics

Standard deviation497.62741
Coefficient of variation (CV)0.80526867
Kurtosis-1.0775255
Mean617.96445
Median Absolute Deviation (MAD)349
Skewness0.52420967
Sum1199469
Variance247633.04
MonotonicityNot monotonic
2023-05-18T20:08:15.589452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
212 23
 
1.2%
214 22
 
1.1%
218 21
 
1.1%
216 19
 
1.0%
194 14
 
0.7%
211 13
 
0.7%
192 12
 
0.6%
209 11
 
0.6%
193 10
 
0.5%
210 9
 
0.5%
Other values (984) 1787
92.1%
ValueCountFrequency (%)
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
8 3
 
0.2%
9 2
 
0.1%
10 3
 
0.2%
11 3
 
0.2%
12 4
0.2%
13 5
0.3%
14 8
0.4%
ValueCountFrequency (%)
1713 1
 
0.1%
1712 1
 
0.1%
1696 1
 
0.1%
1694 2
0.1%
1692 1
 
0.1%
1690 1
 
0.1%
1689 1
 
0.1%
1688 3
0.2%
1687 2
0.1%
1686 1
 
0.1%

Y_Minimum
Real number (ℝ)

Distinct1939
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1650684.9
Minimum6712
Maximum12987661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:15.746099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6712
5-th percentile86773
Q1471253
median1204128
Q32183073
95-th percentile4532922
Maximum12987661
Range12980949
Interquartile range (IQR)1711820

Descriptive statistics

Standard deviation1774578.4
Coefficient of variation (CV)1.0750558
Kurtosis11.357575
Mean1650684.9
Median Absolute Deviation (MAD)817349
Skewness2.8112132
Sum3.2039793 × 109
Variance3.1491286 × 1012
MonotonicityNot monotonic
2023-05-18T20:08:15.915059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1803992 2
 
0.1%
28972 2
 
0.1%
270900 1
 
0.1%
430948 1
 
0.1%
409986 1
 
0.1%
402394 1
 
0.1%
393457 1
 
0.1%
379740 1
 
0.1%
375592 1
 
0.1%
363701 1
 
0.1%
Other values (1929) 1929
99.4%
ValueCountFrequency (%)
6712 1
0.1%
7003 1
0.1%
7430 1
0.1%
7851 1
0.1%
9007 1
0.1%
9228 1
0.1%
12799 1
0.1%
13302 1
0.1%
14524 1
0.1%
15184 1
0.1%
ValueCountFrequency (%)
12987661 1
0.1%
12917033 1
0.1%
12806495 1
0.1%
12725281 1
0.1%
12577343 1
0.1%
12438460 1
0.1%
12416454 1
0.1%
11741476 1
0.1%
11569824 1
0.1%
11499942 1
0.1%

Y_Maximum
Real number (ℝ)

Distinct1940
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1650738.7
Minimum6724
Maximum12987692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:16.073077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6724
5-th percentile86815
Q1471281
median1204136
Q32183084
95-th percentile4532948
Maximum12987692
Range12980968
Interquartile range (IQR)1711803

Descriptive statistics

Standard deviation1774590.1
Coefficient of variation (CV)1.0750279
Kurtosis11.357194
Mean1650738.7
Median Absolute Deviation (MAD)817342
Skewness2.811169
Sum3.2040838 × 109
Variance3.14917 × 1012
MonotonicityNot monotonic
2023-05-18T20:08:16.233057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28984 2
 
0.1%
270944 1
 
0.1%
1229628 1
 
0.1%
410023 1
 
0.1%
402418 1
 
0.1%
393488 1
 
0.1%
379759 1
 
0.1%
375611 1
 
0.1%
363718 1
 
0.1%
149070 1
 
0.1%
Other values (1930) 1930
99.4%
ValueCountFrequency (%)
6724 1
0.1%
7020 1
0.1%
7458 1
0.1%
7865 1
0.1%
9033 1
0.1%
9246 1
0.1%
12804 1
0.1%
13320 1
0.1%
14551 1
0.1%
15196 1
0.1%
ValueCountFrequency (%)
12987692 1
0.1%
12917094 1
0.1%
12806520 1
0.1%
12725314 1
0.1%
12577396 1
0.1%
12438491 1
0.1%
12416473 1
0.1%
11741833 1
0.1%
11569844 1
0.1%
11499957 1
0.1%

Pixels_Areas
Real number (ℝ)

Distinct920
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1893.8784
Minimum2
Maximum152655
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:16.389058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile39
Q184
median174
Q3822
95-th percentile11211
Maximum152655
Range152653
Interquartile range (IQR)738

Descriptive statistics

Standard deviation5168.4596
Coefficient of variation (CV)2.7290345
Kurtosis375.8382
Mean1893.8784
Median Absolute Deviation (MAD)114
Skewness14.083822
Sum3676018
Variance26712974
MonotonicityNot monotonic
2023-05-18T20:08:16.536054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 19
 
1.0%
68 19
 
1.0%
60 18
 
0.9%
55 16
 
0.8%
51 15
 
0.8%
16 15
 
0.8%
74 14
 
0.7%
110 14
 
0.7%
63 14
 
0.7%
67 14
 
0.7%
Other values (910) 1783
91.9%
ValueCountFrequency (%)
2 2
 
0.1%
6 2
 
0.1%
8 2
 
0.1%
9 2
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 10
0.5%
14 1
 
0.1%
15 3
 
0.2%
16 15
0.8%
ValueCountFrequency (%)
152655 1
0.1%
37334 1
0.1%
25473 1
0.1%
25323 1
0.1%
24365 1
0.1%
22554 1
0.1%
21987 1
0.1%
21110 1
0.1%
21036 1
0.1%
20894 1
0.1%

X_Perimeter
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct399
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.85523
Minimum2
Maximum10449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:16.701055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q115
median26
Q384
95-th percentile616
Maximum10449
Range10447
Interquartile range (IQR)69

Descriptive statistics

Standard deviation301.20919
Coefficient of variation (CV)2.6928485
Kurtosis715.95655
Mean111.85523
Median Absolute Deviation (MAD)15
Skewness21.539451
Sum217111
Variance90726.974
MonotonicityNot monotonic
2023-05-18T20:08:16.852213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 81
 
4.2%
15 75
 
3.9%
13 71
 
3.7%
14 69
 
3.6%
11 58
 
3.0%
16 57
 
2.9%
10 55
 
2.8%
9 52
 
2.7%
17 51
 
2.6%
18 51
 
2.6%
Other values (389) 1321
68.1%
ValueCountFrequency (%)
2 2
 
0.1%
3 2
 
0.1%
4 6
 
0.3%
5 10
 
0.5%
6 18
 
0.9%
7 14
 
0.7%
8 28
1.4%
9 52
2.7%
10 55
2.8%
11 58
3.0%
ValueCountFrequency (%)
10449 1
0.1%
1275 1
0.1%
1193 1
0.1%
1169 1
0.1%
1138 1
0.1%
1084 1
0.1%
1050 1
0.1%
1022 1
0.1%
1021 1
0.1%
1015 1
0.1%

Y_Perimeter
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct317
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.965997
Minimum1
Maximum18152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:17.003778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q113
median25
Q383
95-th percentile381
Maximum18152
Range18151
Interquartile range (IQR)70

Descriptive statistics

Standard deviation426.48288
Coefficient of variation (CV)5.1404539
Kurtosis1663.0518
Mean82.965997
Median Absolute Deviation (MAD)14
Skewness39.293158
Sum161037
Variance181887.65
MonotonicityNot monotonic
2023-05-18T20:08:17.157393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 87
 
4.5%
12 78
 
4.0%
10 72
 
3.7%
13 61
 
3.1%
14 60
 
3.1%
17 55
 
2.8%
15 54
 
2.8%
20 45
 
2.3%
8 44
 
2.3%
16 43
 
2.2%
Other values (307) 1342
69.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 2
 
0.1%
3 9
 
0.5%
4 35
1.8%
5 17
 
0.9%
6 19
 
1.0%
7 35
1.8%
8 44
2.3%
9 35
1.8%
10 72
3.7%
ValueCountFrequency (%)
18152 1
0.1%
903 1
0.1%
712 1
0.1%
709 1
0.1%
696 1
0.1%
684 1
0.1%
680 1
0.1%
605 1
0.1%
604 1
0.1%
597 1
0.1%

Sum_of_Luminosity
Real number (ℝ)

Distinct1909
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206312.15
Minimum250
Maximum11591414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:17.306401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile4347
Q19522
median19202
Q383011
95-th percentile1293558
Maximum11591414
Range11591164
Interquartile range (IQR)73489

Descriptive statistics

Standard deviation512293.59
Coefficient of variation (CV)2.4830995
Kurtosis131.49526
Mean206312.15
Median Absolute Deviation (MAD)12505
Skewness7.73072
Sum4.0045188 × 108
Variance2.6244472 × 1011
MonotonicityNot monotonic
2023-05-18T20:08:17.457399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8140 2
 
0.1%
41476 2
 
0.1%
13352 2
 
0.1%
16308 2
 
0.1%
6216 2
 
0.1%
10024 2
 
0.1%
29002 2
 
0.1%
13351 2
 
0.1%
11890 2
 
0.1%
7502 2
 
0.1%
Other values (1899) 1921
99.0%
ValueCountFrequency (%)
250 1
0.1%
255 1
0.1%
718 1
0.1%
764 1
0.1%
775 1
0.1%
950 1
0.1%
958 1
0.1%
1059 1
0.1%
1063 1
0.1%
1233 1
0.1%
ValueCountFrequency (%)
11591414 1
0.1%
3918209 1
0.1%
3061597 1
0.1%
3037459 1
0.1%
2935414 1
0.1%
2712104 1
0.1%
2638402 1
0.1%
2554885 1
0.1%
2529140 1
0.1%
2519511 1
0.1%

Minimum_of_Luminosity
Real number (ℝ)

Distinct161
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.548686
Minimum0
Maximum203
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:17.614403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q163
median90
Q3106
95-th percentile124
Maximum203
Range203
Interquartile range (IQR)43

Descriptive statistics

Standard deviation32.134276
Coefficient of variation (CV)0.3800683
Kurtosis0.11237035
Mean84.548686
Median Absolute Deviation (MAD)20
Skewness-0.10709776
Sum164109
Variance1032.6117
MonotonicityNot monotonic
2023-05-18T20:08:17.770179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 39
 
2.0%
91 38
 
2.0%
97 38
 
2.0%
104 37
 
1.9%
96 37
 
1.9%
95 36
 
1.9%
99 36
 
1.9%
84 35
 
1.8%
105 34
 
1.8%
120 33
 
1.7%
Other values (151) 1578
81.3%
ValueCountFrequency (%)
0 4
0.2%
4 1
 
0.1%
6 2
0.1%
7 1
 
0.1%
9 1
 
0.1%
11 2
0.1%
12 1
 
0.1%
14 1
 
0.1%
15 1
 
0.1%
16 2
0.1%
ValueCountFrequency (%)
203 1
 
0.1%
196 1
 
0.1%
195 2
0.1%
192 2
0.1%
191 1
 
0.1%
190 1
 
0.1%
179 2
0.1%
178 4
0.2%
177 1
 
0.1%
175 1
 
0.1%

Maximum_of_Luminosity
Real number (ℝ)

Distinct100
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.19371
Minimum37
Maximum253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:17.934176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile102
Q1124
median127
Q3140
95-th percentile156
Maximum253
Range216
Interquartile range (IQR)16

Descriptive statistics

Standard deviation18.690992
Coefficient of variation (CV)0.14356294
Kurtosis7.8584205
Mean130.19371
Median Absolute Deviation (MAD)8
Skewness1.2870354
Sum252706
Variance349.35318
MonotonicityNot monotonic
2023-05-18T20:08:18.082176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127 194
 
10.0%
126 151
 
7.8%
124 146
 
7.5%
141 129
 
6.6%
125 112
 
5.8%
132 109
 
5.6%
143 97
 
5.0%
140 95
 
4.9%
134 95
 
4.9%
135 88
 
4.5%
Other values (90) 725
37.4%
ValueCountFrequency (%)
37 1
 
0.1%
39 1
 
0.1%
70 1
 
0.1%
71 3
 
0.2%
77 2
 
0.1%
78 3
 
0.2%
79 2
 
0.1%
82 1
 
0.1%
84 9
0.5%
85 1
 
0.1%
ValueCountFrequency (%)
253 1
 
0.1%
252 2
 
0.1%
247 1
 
0.1%
236 1
 
0.1%
221 1
 
0.1%
220 1
 
0.1%
213 1
 
0.1%
212 3
0.2%
210 1
 
0.1%
207 5
0.3%

Length_of_Conveyer
Real number (ℝ)

Distinct84
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1459.1602
Minimum1227
Maximum1794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:18.229693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1227
5-th percentile1353
Q11358
median1364
Q31650
95-th percentile1692
Maximum1794
Range567
Interquartile range (IQR)292

Descriptive statistics

Standard deviation144.57782
Coefficient of variation (CV)0.099082898
Kurtosis-1.1917137
Mean1459.1602
Median Absolute Deviation (MAD)11
Skewness0.85142225
Sum2832230
Variance20902.747
MonotonicityNot monotonic
2023-05-18T20:08:18.370702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1358 242
 
12.5%
1356 186
 
9.6%
1360 156
 
8.0%
1362 107
 
5.5%
1364 101
 
5.2%
1692 94
 
4.8%
1353 85
 
4.4%
1687 84
 
4.3%
1354 81
 
4.2%
1387 68
 
3.5%
Other values (74) 737
38.0%
ValueCountFrequency (%)
1227 3
0.2%
1280 1
 
0.1%
1306 4
0.2%
1308 2
0.1%
1320 1
 
0.1%
1322 3
0.2%
1324 1
 
0.1%
1333 2
0.1%
1336 1
 
0.1%
1346 2
0.1%
ValueCountFrequency (%)
1794 1
 
0.1%
1715 2
 
0.1%
1712 1
 
0.1%
1710 6
 
0.3%
1708 3
 
0.2%
1707 1
 
0.1%
1700 2
 
0.1%
1698 19
1.0%
1696 21
1.1%
1694 36
1.9%

TypeOfSteel_A300
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1164 
1
777 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1164
60.0%
1 777
40.0%

Length

2023-05-18T20:08:18.499705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T20:08:18.615703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1164
60.0%
1 777
40.0%

Most occurring characters

ValueCountFrequency (%)
0 1164
60.0%
1 777
40.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1941
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1164
60.0%
1 777
40.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1941
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1164
60.0%
1 777
40.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1164
60.0%
1 777
40.0%

TypeOfSteel_A400
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
1
1164 
0
777 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1164
60.0%
0 777
40.0%

Length

2023-05-18T20:08:18.712730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T20:08:18.829132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1164
60.0%
0 777
40.0%

Most occurring characters

ValueCountFrequency (%)
1 1164
60.0%
0 777
40.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1941
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1164
60.0%
0 777
40.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1941
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1164
60.0%
0 777
40.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1164
60.0%
0 777
40.0%

Steel_Plate_Thickness
Real number (ℝ)

Distinct24
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.737764
Minimum40
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:18.928131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile40
Q140
median70
Q380
95-th percentile200
Maximum300
Range260
Interquartile range (IQR)40

Descriptive statistics

Standard deviation55.086032
Coefficient of variation (CV)0.69961387
Kurtosis4.9378385
Mean78.737764
Median Absolute Deviation (MAD)30
Skewness2.2069351
Sum152830
Variance3034.4709
MonotonicityNot monotonic
2023-05-18T20:08:19.049131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
40 710
36.6%
70 380
19.6%
100 154
 
7.9%
80 150
 
7.7%
50 115
 
5.9%
60 87
 
4.5%
200 79
 
4.1%
300 43
 
2.2%
69 39
 
2.0%
175 33
 
1.7%
Other values (14) 151
 
7.8%
ValueCountFrequency (%)
40 710
36.6%
50 115
 
5.9%
60 87
 
4.5%
69 39
 
2.0%
70 380
19.6%
80 150
 
7.7%
85 4
 
0.2%
90 23
 
1.2%
100 154
 
7.9%
120 25
 
1.3%
ValueCountFrequency (%)
300 43
2.2%
290 2
 
0.1%
250 2
 
0.1%
220 16
 
0.8%
211 5
 
0.3%
200 79
4.1%
185 14
 
0.7%
180 2
 
0.1%
175 33
1.7%
150 26
 
1.3%

Edges_Index
Real number (ℝ)

Distinct1387
Distinct (%)71.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3317152
Minimum0
Maximum0.9952
Zeros38
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:19.214131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0118
Q10.0604
median0.2273
Q30.5738
95-th percentile0.9029
Maximum0.9952
Range0.9952
Interquartile range (IQR)0.5134

Descriptive statistics

Standard deviation0.29971175
Coefficient of variation (CV)0.9035213
Kurtosis-0.90420928
Mean0.3317152
Median Absolute Deviation (MAD)0.1742
Skewness0.68577108
Sum643.8592
Variance0.089827132
MonotonicityNot monotonic
2023-05-18T20:08:19.364716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0604 43
 
2.2%
0 38
 
2.0%
0.0574 34
 
1.8%
0.0557 30
 
1.5%
0.0585 25
 
1.3%
0.0605 24
 
1.2%
0.0556 21
 
1.1%
0.0586 21
 
1.1%
0.0575 20
 
1.0%
0.0558 12
 
0.6%
Other values (1377) 1673
86.2%
ValueCountFrequency (%)
0 38
2.0%
0.0012 2
 
0.1%
0.0014 2
 
0.1%
0.0015 4
 
0.2%
0.0023 1
 
0.1%
0.0024 8
 
0.4%
0.003 3
 
0.2%
0.0035 1
 
0.1%
0.0036 2
 
0.1%
0.0043 1
 
0.1%
ValueCountFrequency (%)
0.9952 1
 
0.1%
0.9923 1
 
0.1%
0.9905 1
 
0.1%
0.9897 1
 
0.1%
0.9846 2
0.1%
0.9835 1
 
0.1%
0.9834 1
 
0.1%
0.9816 1
 
0.1%
0.9808 3
0.2%
0.9795 1
 
0.1%

Empty_Index
Real number (ℝ)

Distinct1338
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41420335
Minimum0
Maximum0.9439
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:19.513716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2083
Q10.3158
median0.4121
Q30.5016
95-th percentile0.6448
Maximum0.9439
Range0.9439
Interquartile range (IQR)0.1858

Descriptive statistics

Standard deviation0.13726149
Coefficient of variation (CV)0.33138672
Kurtosis0.18930051
Mean0.41420335
Median Absolute Deviation (MAD)0.0929
Skewness0.29346776
Sum803.9687
Variance0.018840716
MonotonicityNot monotonic
2023-05-18T20:08:19.656717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3333 28
 
1.4%
0.25 21
 
1.1%
0.2222 12
 
0.6%
0.3636 12
 
0.6%
0.2 12
 
0.6%
0.375 11
 
0.6%
0.4 11
 
0.6%
0.5 10
 
0.5%
0.3 10
 
0.5%
0.2778 9
 
0.5%
Other values (1328) 1805
93.0%
ValueCountFrequency (%)
0 2
0.1%
0.0278 1
0.1%
0.0368 1
0.1%
0.0595 1
0.1%
0.0682 1
0.1%
0.0714 1
0.1%
0.0781 1
0.1%
0.0818 1
0.1%
0.0926 1
0.1%
0.0972 1
0.1%
ValueCountFrequency (%)
0.9439 1
0.1%
0.9275 1
0.1%
0.894 1
0.1%
0.8888 1
0.1%
0.8856 1
0.1%
0.8817 1
0.1%
0.8767 1
0.1%
0.8648 1
0.1%
0.8487 1
0.1%
0.8473 1
0.1%

Square_Index
Real number (ℝ)

Distinct770
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57076713
Minimum0.0083
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:19.806511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0083
5-th percentile0.125
Q10.3613
median0.5556
Q30.8182
95-th percentile0.9912
Maximum1
Range0.9917
Interquartile range (IQR)0.4569

Descriptive statistics

Standard deviation0.27105839
Coefficient of variation (CV)0.47490188
Kurtosis-1.1580304
Mean0.57076713
Median Absolute Deviation (MAD)0.2223
Skewness-0.056305677
Sum1107.859
Variance0.073472648
MonotonicityNot monotonic
2023-05-18T20:08:19.969520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 91
 
4.7%
0.8 42
 
2.2%
0.5 38
 
2.0%
0.6667 37
 
1.9%
0.3333 31
 
1.6%
0.75 30
 
1.5%
0.8889 28
 
1.4%
0.9091 27
 
1.4%
0.8571 25
 
1.3%
0.4 24
 
1.2%
Other values (760) 1568
80.8%
ValueCountFrequency (%)
0.0083 1
0.1%
0.009 1
0.1%
0.0261 1
0.1%
0.0294 1
0.1%
0.0393 1
0.1%
0.0396 1
0.1%
0.0408 1
0.1%
0.0422 1
0.1%
0.0441 1
0.1%
0.0448 1
0.1%
ValueCountFrequency (%)
1 91
4.7%
0.9955 1
 
0.1%
0.9945 1
 
0.1%
0.9942 3
 
0.2%
0.993 1
 
0.1%
0.9912 1
 
0.1%
0.9895 1
 
0.1%
0.9884 1
 
0.1%
0.9867 1
 
0.1%
0.9832 1
 
0.1%

Outside_X_Index
Real number (ℝ)

Distinct454
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.033361103
Minimum0.0015
Maximum0.8759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:20.121520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.0044
Q10.0066
median0.0101
Q30.0235
95-th percentile0.1289
Maximum0.8759
Range0.8744
Interquartile range (IQR)0.0169

Descriptive statistics

Standard deviation0.058961169
Coefficient of variation (CV)1.7673627
Kurtosis46.109128
Mean0.033361103
Median Absolute Deviation (MAD)0.0047
Skewness5.1818301
Sum64.7539
Variance0.0034764195
MonotonicityNot monotonic
2023-05-18T20:08:20.268624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0059 75
 
3.9%
0.0066 71
 
3.7%
0.0081 53
 
2.7%
0.0088 48
 
2.5%
0.0053 46
 
2.4%
0.0074 45
 
2.3%
0.0044 42
 
2.2%
0.0047 42
 
2.2%
0.0065 40
 
2.1%
0.0052 40
 
2.1%
Other values (444) 1439
74.1%
ValueCountFrequency (%)
0.0015 2
 
0.1%
0.0022 3
 
0.2%
0.0024 1
 
0.1%
0.0029 4
 
0.2%
0.003 6
 
0.3%
0.0035 5
 
0.3%
0.0036 8
 
0.4%
0.0037 29
1.5%
0.0041 20
1.0%
0.0042 2
 
0.1%
ValueCountFrequency (%)
0.8759 1
0.1%
0.6226 1
0.1%
0.6209 1
0.1%
0.5906 1
0.1%
0.5692 1
0.1%
0.4964 1
0.1%
0.4957 1
0.1%
0.4698 1
0.1%
0.4177 1
0.1%
0.3878 1
0.1%

Edges_X_Index
Real number (ℝ)

Distinct818
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61052865
Minimum0.0144
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:20.420891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0144
5-th percentile0.2105
Q10.4118
median0.6364
Q30.8
95-th percentile1
Maximum1
Range0.9856
Interquartile range (IQR)0.3882

Descriptive statistics

Standard deviation0.24327692
Coefficient of variation (CV)0.3984693
Kurtosis-0.9302592
Mean0.61052865
Median Absolute Deviation (MAD)0.1819
Skewness-0.23509587
Sum1185.0361
Variance0.059183659
MonotonicityNot monotonic
2023-05-18T20:08:20.573460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 135
 
7.0%
0.8 49
 
2.5%
0.75 43
 
2.2%
0.6667 41
 
2.1%
0.5 33
 
1.7%
0.8333 24
 
1.2%
0.9 21
 
1.1%
0.8571 21
 
1.1%
0.7778 20
 
1.0%
0.8889 20
 
1.0%
Other values (808) 1534
79.0%
ValueCountFrequency (%)
0.0144 1
0.1%
0.0645 1
0.1%
0.0657 1
0.1%
0.0717 1
0.1%
0.0724 1
0.1%
0.0782 1
0.1%
0.0794 1
0.1%
0.0874 1
0.1%
0.0909 1
0.1%
0.0968 1
0.1%
ValueCountFrequency (%)
1 135
7.0%
0.9965 1
 
0.1%
0.9879 1
 
0.1%
0.9828 1
 
0.1%
0.9804 1
 
0.1%
0.9776 1
 
0.1%
0.975 1
 
0.1%
0.9688 2
 
0.1%
0.9682 1
 
0.1%
0.9671 1
 
0.1%

Edges_Y_Index
Real number (ℝ)

Distinct648
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81347223
Minimum0.0484
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:20.725281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0484
5-th percentile0.381
Q10.5968
median0.9474
Q31
95-th percentile1
Maximum1
Range0.9516
Interquartile range (IQR)0.4032

Descriptive statistics

Standard deviation0.23427362
Coefficient of variation (CV)0.28799216
Kurtosis-0.56319361
Mean0.81347223
Median Absolute Deviation (MAD)0.0526
Skewness-0.92858241
Sum1578.9496
Variance0.05488413
MonotonicityNot monotonic
2023-05-18T20:08:20.877650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 820
42.2%
0.6667 19
 
1.0%
0.9231 16
 
0.8%
0.8333 16
 
0.8%
0.75 16
 
0.8%
0.9091 14
 
0.7%
0.8 14
 
0.7%
0.9167 13
 
0.7%
0.9 12
 
0.6%
0.875 12
 
0.6%
Other values (638) 989
51.0%
ValueCountFrequency (%)
0.0484 1
0.1%
0.105 1
0.1%
0.1123 1
0.1%
0.1312 1
0.1%
0.1321 1
0.1%
0.1378 1
0.1%
0.1379 1
0.1%
0.1463 1
0.1%
0.1509 2
0.1%
0.1521 1
0.1%
ValueCountFrequency (%)
1 820
42.2%
0.9994 1
 
0.1%
0.9977 1
 
0.1%
0.9973 1
 
0.1%
0.9931 2
 
0.1%
0.9925 1
 
0.1%
0.9921 2
 
0.1%
0.992 1
 
0.1%
0.9917 1
 
0.1%
0.9916 1
 
0.1%
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
1.0
1072 
0.0
778 
0.5
 
91

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5823
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1072
55.2%
0.0 778
40.1%
0.5 91
 
4.7%

Length

2023-05-18T20:08:21.006809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T20:08:21.123431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1072
55.2%
0.0 778
40.1%
0.5 91
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 2719
46.7%
. 1941
33.3%
1 1072
 
18.4%
5 91
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3882
66.7%
Other Punctuation 1941
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2719
70.0%
1 1072
 
27.6%
5 91
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 1941
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2719
46.7%
. 1941
33.3%
1 1072
 
18.4%
5 91
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2719
46.7%
. 1941
33.3%
1 1072
 
18.4%
5 91
 
1.6%

LogOfAreas
Real number (ℝ)

Distinct914
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4923884
Minimum0.301
Maximum5.1837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:21.237435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.301
5-th percentile1.5911
Q11.9243
median2.2406
Q32.9149
95-th percentile4.0496
Maximum5.1837
Range4.8827
Interquartile range (IQR)0.9906

Descriptive statistics

Standard deviation0.78892985
Coefficient of variation (CV)0.31653568
Kurtosis-0.33921066
Mean2.4923884
Median Absolute Deviation (MAD)0.4017
Skewness0.74828448
Sum4837.7258
Variance0.62241031
MonotonicityNot monotonic
2023-05-18T20:08:21.393623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8325 19
 
1.0%
1.716 19
 
1.0%
1.7781 18
 
0.9%
1.7404 16
 
0.8%
1.7076 15
 
0.8%
1.2041 15
 
0.8%
1.7993 14
 
0.7%
1.7482 14
 
0.7%
1.8261 14
 
0.7%
1.8692 14
 
0.7%
Other values (904) 1783
91.9%
ValueCountFrequency (%)
0.301 2
 
0.1%
0.7782 2
 
0.1%
0.9031 2
 
0.1%
0.9542 2
 
0.1%
1 1
 
0.1%
1.0414 1
 
0.1%
1.0792 10
0.5%
1.1461 1
 
0.1%
1.1761 3
 
0.2%
1.2041 15
0.8%
ValueCountFrequency (%)
5.1837 1
0.1%
4.5721 1
0.1%
4.4061 1
0.1%
4.4035 1
0.1%
4.3868 1
0.1%
4.3532 1
0.1%
4.3422 1
0.1%
4.3245 1
0.1%
4.323 1
0.1%
4.32 1
0.1%

Log_X_Index
Real number (ℝ)

Distinct183
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3356861
Minimum0.301
Maximum3.0741
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:21.538633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.301
5-th percentile0.7782
Q11
median1.1761
Q31.5185
95-th percentile2.243
Maximum3.0741
Range2.7731
Interquartile range (IQR)0.5185

Descriptive statistics

Standard deviation0.48161161
Coefficient of variation (CV)0.36057244
Kurtosis-0.04144811
Mean1.3356861
Median Absolute Deviation (MAD)0.2219
Skewness1.0010141
Sum2592.5668
Variance0.23194974
MonotonicityNot monotonic
2023-05-18T20:08:21.676632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9542 147
 
7.6%
1.0792 120
 
6.2%
0.9031 119
 
6.1%
1 116
 
6.0%
1.0414 112
 
5.8%
1.1139 90
 
4.6%
0.8451 81
 
4.2%
1.1461 70
 
3.6%
1.2305 58
 
3.0%
0.7782 58
 
3.0%
Other values (173) 970
50.0%
ValueCountFrequency (%)
0.301 2
 
0.1%
0.4771 3
 
0.2%
0.6021 10
 
0.5%
0.699 29
 
1.5%
0.7782 58
 
3.0%
0.8451 81
4.2%
0.9031 119
6.1%
0.9542 147
7.6%
1 116
6.0%
1.0414 112
5.8%
ValueCountFrequency (%)
3.0741 1
0.1%
2.9385 1
0.1%
2.9335 1
0.1%
2.918 1
0.1%
2.8882 1
0.1%
2.842 1
0.1%
2.8414 1
0.1%
2.8048 1
0.1%
2.7543 1
0.1%
2.7235 1
0.1%

Log_Y_Index
Real number (ℝ)

Distinct217
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4032713
Minimum0
Maximum4.2587
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:21.822635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.699
Q11.0792
median1.3222
Q31.7324
95-th percentile2.2304
Maximum4.2587
Range4.2587
Interquartile range (IQR)0.6532

Descriptive statistics

Standard deviation0.45434516
Coefficient of variation (CV)0.32377571
Kurtosis0.38407733
Mean1.4032713
Median Absolute Deviation (MAD)0.2808
Skewness0.44510061
Sum2723.7496
Variance0.20642953
MonotonicityNot monotonic
2023-05-18T20:08:21.958384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0792 92
 
4.7%
1.0414 87
 
4.5%
1 78
 
4.0%
1.1461 70
 
3.6%
1.1139 66
 
3.4%
1.1761 56
 
2.9%
0.9542 56
 
2.9%
0.9031 53
 
2.7%
1.301 51
 
2.6%
1.2553 47
 
2.4%
Other values (207) 1285
66.2%
ValueCountFrequency (%)
0 2
 
0.1%
0.301 6
 
0.3%
0.4771 23
 
1.2%
0.6021 44
2.3%
0.699 31
 
1.6%
0.7782 28
 
1.4%
0.8451 31
 
1.6%
0.9031 53
2.7%
0.9542 56
2.9%
1 78
4.0%
ValueCountFrequency (%)
4.2587 1
0.1%
2.776 1
0.1%
2.6294 1
0.1%
2.6181 1
0.1%
2.6149 1
0.1%
2.5922 1
0.1%
2.5752 2
0.1%
2.5527 1
0.1%
2.5515 1
0.1%
2.5052 1
0.1%

Orientation_Index
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct918
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.083287635
Minimum-0.991
Maximum0.9917
Zeros91
Zeros (%)4.7%
Negative778
Negative (%)40.1%
Memory size15.3 KiB
2023-05-18T20:08:22.109411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-0.991
5-th percentile-0.6713
Q1-0.3333
median0.0952
Q30.5116
95-th percentile0.8372
Maximum0.9917
Range1.9827
Interquartile range (IQR)0.8449

Descriptive statistics

Standard deviation0.50086805
Coefficient of variation (CV)6.0137143
Kurtosis-1.0446547
Mean0.083287635
Median Absolute Deviation (MAD)0.4181
Skewness-0.15344552
Sum161.6613
Variance0.2508688
MonotonicityNot monotonic
2023-05-18T20:08:22.264781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 91
 
4.7%
0.3333 23
 
1.2%
0.6667 21
 
1.1%
-0.2 21
 
1.1%
0.5 21
 
1.1%
0.2 21
 
1.1%
0.25 20
 
1.0%
0.1818 17
 
0.9%
-0.5 17
 
0.9%
0.1111 15
 
0.8%
Other values (908) 1674
86.2%
ValueCountFrequency (%)
-0.991 1
0.1%
-0.9739 1
0.1%
-0.9706 1
0.1%
-0.9604 1
0.1%
-0.9592 1
0.1%
-0.9559 1
0.1%
-0.9546 1
0.1%
-0.9514 1
0.1%
-0.9509 1
0.1%
-0.95 2
0.1%
ValueCountFrequency (%)
0.9917 1
0.1%
0.9607 1
0.1%
0.9578 1
0.1%
0.9552 1
0.1%
0.9481 1
0.1%
0.9467 1
0.1%
0.9463 1
0.1%
0.9431 1
0.1%
0.9419 1
0.1%
0.9388 1
0.1%

Luminosity_Index
Real number (ℝ)

Distinct1522
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.13130505
Minimum-0.9989
Maximum0.6421
Zeros1
Zeros (%)0.1%
Negative1714
Negative (%)88.3%
Memory size15.3 KiB
2023-05-18T20:08:22.423539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-0.9989
5-th percentile-0.3479
Q1-0.195
median-0.133
Q3-0.0666
95-th percentile0.0376
Maximum0.6421
Range1.641
Interquartile range (IQR)0.1284

Descriptive statistics

Standard deviation0.14876684
Coefficient of variation (CV)-1.1329864
Kurtosis5.8067489
Mean-0.13130505
Median Absolute Deviation (MAD)0.063
Skewness0.67933872
Sum-254.8631
Variance0.022131573
MonotonicityNot monotonic
2023-05-18T20:08:22.566539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1851 6
 
0.3%
-0.1903 5
 
0.3%
-0.189 5
 
0.3%
-0.1797 4
 
0.2%
-0.112 4
 
0.2%
-0.1805 4
 
0.2%
-0.1865 4
 
0.2%
-0.1078 4
 
0.2%
-0.0935 4
 
0.2%
-0.0481 4
 
0.2%
Other values (1512) 1897
97.7%
ValueCountFrequency (%)
-0.9989 1
0.1%
-0.885 1
0.1%
-0.8603 1
0.1%
-0.6332 1
0.1%
-0.6096 1
0.1%
-0.6017 1
0.1%
-0.5971 1
0.1%
-0.594 1
0.1%
-0.5902 1
0.1%
-0.585 1
0.1%
ValueCountFrequency (%)
0.6421 1
0.1%
0.5917 1
0.1%
0.5916 1
0.1%
0.5909 1
0.1%
0.5799 1
0.1%
0.5613 1
0.1%
0.5591 1
0.1%
0.5552 1
0.1%
0.5518 1
0.1%
0.5237 1
0.1%

SigmoidOfAreas
Real number (ℝ)

Distinct388
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58542045
Minimum0.119
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-05-18T20:08:22.719540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.119
5-th percentile0.1696
Q10.2482
median0.5063
Q30.9998
95-th percentile1
Maximum1
Range0.881
Interquartile range (IQR)0.7516

Descriptive statistics

Standard deviation0.33945181
Coefficient of variation (CV)0.57984275
Kurtosis-1.7076941
Mean0.58542045
Median Absolute Deviation (MAD)0.3098
Skewness0.12578852
Sum1136.3011
Variance0.11522753
MonotonicityNot monotonic
2023-05-18T20:08:22.859270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 460
 
23.7%
0.1773 29
 
1.5%
0.2288 29
 
1.5%
0.2173 25
 
1.3%
0.1954 24
 
1.2%
0.2432 24
 
1.2%
0.2901 23
 
1.2%
0.2051 22
 
1.1%
0.9999 22
 
1.1%
0.3068 21
 
1.1%
Other values (378) 1262
65.0%
ValueCountFrequency (%)
0.119 2
 
0.1%
0.124 1
 
0.1%
0.1262 4
 
0.2%
0.1284 2
 
0.1%
0.1292 4
 
0.2%
0.1307 2
 
0.1%
0.1322 11
0.6%
0.133 1
 
0.1%
0.1353 5
0.3%
0.1361 2
 
0.1%
ValueCountFrequency (%)
1 460
23.7%
0.9999 22
 
1.1%
0.9998 16
 
0.8%
0.9997 5
 
0.3%
0.9996 4
 
0.2%
0.9995 2
 
0.1%
0.9994 2
 
0.1%
0.9993 3
 
0.2%
0.9992 2
 
0.1%
0.9991 2
 
0.1%

Pastry
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1783 
1
 
158

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1783
91.9%
1 158
 
8.1%

Length

2023-05-18T20:08:22.979687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T20:08:23.093685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1783
91.9%
1 158
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0 1783
91.9%
1 158
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1941
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1783
91.9%
1 158
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1941
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1783
91.9%
1 158
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1783
91.9%
1 158
 
8.1%

Z_Scratch
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1751 
1
190 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1751
90.2%
1 190
 
9.8%

Length

2023-05-18T20:08:23.185716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T20:08:23.295716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1751
90.2%
1 190
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0 1751
90.2%
1 190
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1941
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1751
90.2%
1 190
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1941
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1751
90.2%
1 190
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1751
90.2%
1 190
 
9.8%

K_Scatch
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1550 
1
391 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1550
79.9%
1 391
 
20.1%

Length

2023-05-18T20:08:23.385954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T20:08:23.494955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1550
79.9%
1 391
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0 1550
79.9%
1 391
 
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1941
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1550
79.9%
1 391
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1941
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1550
79.9%
1 391
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1550
79.9%
1 391
 
20.1%

Stains
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1869 
1
 
72

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1869
96.3%
1 72
 
3.7%

Length

2023-05-18T20:08:23.589954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T20:08:23.698954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1869
96.3%
1 72
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 1869
96.3%
1 72
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1941
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1869
96.3%
1 72
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1941
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1869
96.3%
1 72
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1869
96.3%
1 72
 
3.7%

Dirtiness
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1886 
1
 
55

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1886
97.2%
1 55
 
2.8%

Length

2023-05-18T20:08:23.789979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T20:08:23.898642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1886
97.2%
1 55
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 1886
97.2%
1 55
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1941
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1886
97.2%
1 55
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1941
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1886
97.2%
1 55
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1886
97.2%
1 55
 
2.8%

Bumps
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1539 
1
402 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1539
79.3%
1 402
 
20.7%

Length

2023-05-18T20:08:23.987643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T20:08:24.098642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1539
79.3%
1 402
 
20.7%

Most occurring characters

ValueCountFrequency (%)
0 1539
79.3%
1 402
 
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1941
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1539
79.3%
1 402
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1941
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1539
79.3%
1 402
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1539
79.3%
1 402
 
20.7%

Other_Faults
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1268 
1
673 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1268
65.3%
1 673
34.7%

Length

2023-05-18T20:08:24.192643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T20:08:24.307643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1268
65.3%
1 673
34.7%

Most occurring characters

ValueCountFrequency (%)
0 1268
65.3%
1 673
34.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1941
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1268
65.3%
1 673
34.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1941
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1268
65.3%
1 673
34.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1268
65.3%
1 673
34.7%

Interactions

2023-05-18T20:08:10.524436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:56.124580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:59.268903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:02.470222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:05.569932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:08.781850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:12.302536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:17.429131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:20.679221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:23.978628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:27.718672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:30.746519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:33.806274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:36.769660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:40.151525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:43.060271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:46.026299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:48.949297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:51.921564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:55.397896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:58.341900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:01.340547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:04.234605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:07.441185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:10.657091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:56.256248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:59.404902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:02.600221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:05.721330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:08.945706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:12.435839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:17.564277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:20.834742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:24.127629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:27.845206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:30.884160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:33.934354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:36.895662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:40.277527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:43.184275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:46.157523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:49.079296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:52.052527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:55.521898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:58.474059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:01.461686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:04.377607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:07.572198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:10.791458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:56.394248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:59.536905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:02.738035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:05.866646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:09.084707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:12.567839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:17.704276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:20.979104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:24.270353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:27.975206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:31.020049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:34.061354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:37.028214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:40.401524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:43.311273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:46.282102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:49.204409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:52.178456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:55.651032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:58.608062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:01.586688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:04.513741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:07.707200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:10.920310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:56.525249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:59.675591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:02.864035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:06.032313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:09.219705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:14.621346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:17.836279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:21.123750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:24.410355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:28.101203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:31.149050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:34.186356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:37.151131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:40.524202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:43.434271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:46.400102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:49.326408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:52.300826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:55.780557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:58.736063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:01.711685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:04.650740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:07.857197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:11.040403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:56.665387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:59.806589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:02.995035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:06.159421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:09.355425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:14.774347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:17.973280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:21.266751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:24.544355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:28.229002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:31.279050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:34.311357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:37.274130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:40.650932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:43.555406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:46.520103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:49.448406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:52.418826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:55.909813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:58.862849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:01.831687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:04.785741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:07.985197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:11.171616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:56.802385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:59.947586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:03.138034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:06.299253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:09.500426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:14.914346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:18.124256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:21.408749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:24.694244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:28.362459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:31.414302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:34.445485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:37.409129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:40.798025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:43.691848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:46.661411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:49.586406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:52.550827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:56.044082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:59.000247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-18T20:08:13.212537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:58.227219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:01.434824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:04.540300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:07.725446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:11.136506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:16.407283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:19.607774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:22.948176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:26.623818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:29.768395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:32.819553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:35.796422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-18T20:07:42.119795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:45.046702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:48.008879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:50.954566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:54.463440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:57.376867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:00.361864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:03.290960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:06.393125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:09.535254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:13.326537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:58.352808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:01.562826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:04.663300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:07.845557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:11.274535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:16.528605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:19.746772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:23.074178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:26.755578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:29.891633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:32.939550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:35.923044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:39.305763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:42.236796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:45.162946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:48.126880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:51.073214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:54.577441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:57.493865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:00.480038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:03.410961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:06.525364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:09.661382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:13.441539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:58.478810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:01.688174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:04.790136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:07.964555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:11.409318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:16.664605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:19.874775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:23.198879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:26.885577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:30.005634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:33.059547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:36.039041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:39.417762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:42.353794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:45.280948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:48.240138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:51.189215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:54.690443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:57.606866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:00.598638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:03.524211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:06.652973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:09.781379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:13.556538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:58.598811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:01.815693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:04.914136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:08.081554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:11.541318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:16.781605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:19.998775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:23.318881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:27.025696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:30.122634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:33.175549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:36.151042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:39.532873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:42.461798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:45.396949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:48.353138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:51.301447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:54.802392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:57.718866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:00.720777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:03.637212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:06.776974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:09.900379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:13.677174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:58.735000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:01.943693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:05.044152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:08.207554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:11.684319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:16.906606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:20.128494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:23.443882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:27.174417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:30.246633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:33.298549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:36.271490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:39.651872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:42.579536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:45.519946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:48.468138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:51.417446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:54.915392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:57.833864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:00.845452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:03.760210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:06.906973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:10.023383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:13.794175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:58.860001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:02.068948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:05.167151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:08.323771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:11.826318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:17.027541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:20.252489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:23.577879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:27.297505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:30.359750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:33.413271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:36.382490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:39.768873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:42.691533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:45.636383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:48.579139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:51.528445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:55.026916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:57.943169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:00.966923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:03.870211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:07.039024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:10.140433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:13.932177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:59.005003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:02.216223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:05.310332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:08.478845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:11.997095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:17.169128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:20.395490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:23.725650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:27.439506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:30.500751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:33.554271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:36.519660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:39.902871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:42.826534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:45.779265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:48.714296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:51.668444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:55.167750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:58.091692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:01.102546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:04.001606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:07.184185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:10.278435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:14.056175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:06:59.145907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:02.348223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:05.448332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:08.614849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:12.160063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:17.300131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:20.530490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:23.858627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:27.583506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:30.626517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:33.688270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:36.650662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:40.034529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:42.947534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:45.910275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:48.840300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:51.803553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:55.289660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:07:58.222739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:01.227547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:04.125606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:07.317186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T20:08:10.402435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-18T20:08:24.459489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
X_MinimumX_MaximumY_MinimumY_MaximumPixels_AreasX_PerimeterY_PerimeterSum_of_LuminosityMinimum_of_LuminosityMaximum_of_LuminosityLength_of_ConveyerSteel_Plate_ThicknessEdges_IndexEmpty_IndexSquare_IndexOutside_X_IndexEdges_X_IndexEdges_Y_IndexLogOfAreasLog_X_IndexLog_Y_IndexOrientation_IndexLuminosity_IndexSigmoidOfAreasTypeOfSteel_A300TypeOfSteel_A400Outside_Global_IndexPastryZ_ScratchK_ScatchStainsDirtinessBumpsOther_Faults
X_Minimum1.0000.949-0.004-0.004-0.416-0.409-0.412-0.4200.284-0.0780.2460.2510.459-0.2150.086-0.4160.1920.368-0.416-0.392-0.3830.1480.008-0.4030.1990.1990.1630.1900.2500.5150.1940.3290.2830.207
X_Maximum0.9491.0000.0320.032-0.269-0.268-0.276-0.2760.135-0.0590.2550.1440.391-0.1620.081-0.2730.1650.236-0.269-0.245-0.2630.049-0.018-0.2740.3150.3150.2470.2150.3590.6220.1690.3190.2560.236
Y_Minimum-0.0040.0321.0001.0000.0450.0760.0190.024-0.113-0.062-0.009-0.196-0.027-0.0040.0180.1020.036-0.0690.0450.1030.001-0.130-0.0770.0380.1960.1960.0870.1210.1060.1980.0630.2560.1940.206
Y_Maximum-0.0040.0321.0001.0000.0450.0760.0190.024-0.113-0.062-0.009-0.196-0.027-0.0040.0180.1020.036-0.0690.0450.1030.001-0.130-0.0770.0380.1960.1960.0870.1210.1060.1980.0630.2560.1940.206
Pixels_Areas-0.416-0.2690.0450.0451.0000.8960.9490.985-0.5840.004-0.056-0.285-0.3860.354-0.2470.784-0.536-0.5511.0000.7900.9090.006-0.2250.9770.1010.1010.0260.0210.0300.2760.0000.0000.0660.090
X_Perimeter-0.409-0.2680.0760.0760.8961.0000.8810.895-0.4470.094-0.099-0.278-0.3520.579-0.2590.888-0.578-0.7460.8960.8890.779-0.192-0.0850.9280.0040.0040.0390.0000.0000.0930.0000.0000.0000.000
Y_Perimeter-0.412-0.2760.0190.0190.9490.8811.0000.939-0.5100.008-0.096-0.236-0.4200.471-0.3420.689-0.685-0.5480.9490.6860.9570.146-0.1810.9560.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Sum_of_Luminosity-0.420-0.2760.0240.0240.9850.8950.9391.000-0.4960.111-0.067-0.309-0.3520.368-0.2470.791-0.526-0.5740.9850.7950.890-0.014-0.1050.9650.1810.1810.0540.0460.0670.4610.0160.0000.1170.160
Minimum_of_Luminosity0.2840.135-0.113-0.113-0.584-0.447-0.510-0.4961.0000.415-0.0990.1270.3980.0380.056-0.4020.2060.188-0.584-0.429-0.5490.0050.717-0.5160.3800.3800.2070.1870.1270.7020.2770.1270.2790.258
Maximum_of_Luminosity-0.078-0.059-0.062-0.0620.0040.0940.0080.1110.4151.0000.052-0.1610.1530.1450.1110.1310.007-0.2250.0040.127-0.051-0.1850.853-0.0030.3110.3110.1330.0390.2350.2440.0950.0620.1080.144
Length_of_Conveyer0.2460.255-0.009-0.009-0.056-0.099-0.096-0.067-0.0990.0521.0000.1940.125-0.1660.157-0.1820.0570.127-0.056-0.064-0.0640.072-0.037-0.0920.4340.4340.1070.1950.2490.5830.1410.0730.2960.237
Steel_Plate_Thickness0.2510.144-0.196-0.196-0.285-0.278-0.236-0.3090.127-0.1610.1941.0000.174-0.069-0.102-0.362-0.0080.327-0.285-0.333-0.1860.317-0.158-0.2540.7240.7240.1700.2020.4280.5270.1930.2770.1640.322
Edges_Index0.4590.391-0.027-0.027-0.386-0.352-0.420-0.3520.3980.1530.1250.1741.000-0.1640.170-0.2830.2730.266-0.386-0.281-0.420-0.0290.231-0.3670.1660.1660.1160.0630.1930.4650.2130.3380.2780.205
Empty_Index-0.215-0.162-0.004-0.0040.3540.5790.4710.3680.0380.145-0.166-0.069-0.1641.000-0.0750.530-0.415-0.5720.3540.5110.371-0.1420.1700.4920.1410.1410.1740.1920.1450.2670.0000.0300.2580.053
Square_Index0.0860.0810.0180.018-0.247-0.259-0.342-0.2470.0560.1110.157-0.1020.170-0.0751.000-0.0730.2380.078-0.247-0.060-0.315-0.2260.124-0.2810.2310.2310.3990.2490.0780.3730.1050.2800.2860.165
Outside_X_Index-0.416-0.2730.1020.1020.7840.8880.6890.791-0.4020.131-0.182-0.362-0.2830.530-0.0731.000-0.214-0.8130.7840.9890.551-0.494-0.0360.8160.3320.3320.2450.1170.1280.8190.0600.0420.2170.274
Edges_X_Index0.1920.1650.0360.036-0.536-0.578-0.685-0.5260.2060.0070.057-0.0080.273-0.4150.238-0.2141.0000.186-0.536-0.202-0.730-0.5370.072-0.5560.2060.2060.3290.1640.1720.3130.2620.2610.2410.158
Edges_Y_Index0.3680.236-0.069-0.069-0.551-0.746-0.548-0.5740.188-0.2250.1270.3270.266-0.5720.078-0.8130.1861.000-0.551-0.808-0.3500.574-0.161-0.5970.2820.2820.4120.2410.1490.7150.1270.0780.2250.255
LogOfAreas-0.416-0.2690.0450.0451.0000.8960.9490.985-0.5840.004-0.056-0.285-0.3860.354-0.2470.784-0.536-0.5511.0000.7900.9090.006-0.2250.9770.4200.4200.2880.1690.1410.8100.8510.1270.2890.299
Log_X_Index-0.392-0.2450.1030.1030.7900.8890.6860.795-0.4290.127-0.064-0.333-0.2810.511-0.0600.989-0.202-0.8080.7901.0000.549-0.501-0.0550.8190.3700.3700.3310.2190.1670.8290.4120.1450.2470.305
Log_Y_Index-0.383-0.2630.0010.0010.9090.7790.9570.890-0.549-0.051-0.064-0.186-0.4200.371-0.3150.551-0.730-0.3500.9090.5491.0000.331-0.2500.8990.3560.3560.3200.1700.0610.5890.6000.0510.3300.210
Orientation_Index0.1480.049-0.130-0.1300.006-0.1920.146-0.0140.005-0.1850.0720.317-0.029-0.142-0.226-0.494-0.5370.5740.006-0.5010.3311.000-0.182-0.0180.2660.2660.7840.3860.1430.5550.2530.3030.2860.238
Luminosity_Index0.008-0.018-0.077-0.077-0.225-0.085-0.181-0.1050.7170.853-0.037-0.1580.2310.1700.124-0.0360.072-0.161-0.225-0.055-0.250-0.1821.000-0.2030.2140.2140.1820.1000.1670.2250.3340.0840.1210.118
SigmoidOfAreas-0.403-0.2740.0380.0380.9770.9280.9560.965-0.516-0.003-0.092-0.254-0.3670.492-0.2810.816-0.556-0.5970.9770.8190.899-0.018-0.2031.0000.3310.3310.2100.1190.0640.5600.4210.0710.2510.171
TypeOfSteel_A3000.1990.3150.1960.1960.1010.0040.0000.1810.3800.3110.4340.7240.1660.1410.2310.3320.2060.2820.4200.3700.3560.2660.2140.3311.0000.9990.0700.0480.3370.4060.1500.0760.3040.000
TypeOfSteel_A4000.1990.3150.1960.1960.1010.0040.0000.1810.3800.3110.4340.7240.1660.1410.2310.3320.2060.2820.4200.3700.3560.2660.2140.3310.9991.0000.0700.0480.3370.4060.1500.0760.3040.000
Outside_Global_Index0.1630.2470.0870.0870.0260.0390.0000.0540.2070.1330.1070.1700.1160.1740.3990.2450.3290.4120.2880.3310.3200.7840.1820.2100.0700.0701.0000.2470.0790.2950.2040.1060.1040.021
Pastry0.1900.2150.1210.1210.0210.0000.0000.0460.1870.0390.1950.2020.0630.1920.2490.1170.1640.2410.1690.2190.1700.3860.1000.1190.0480.0480.2471.0000.0920.1450.0480.0390.1480.214
Z_Scratch0.2500.3590.1060.1060.0300.0000.0000.0670.1270.2350.2490.4280.1930.1450.0780.1280.1720.1490.1410.1670.0610.1430.1670.0640.3370.3370.0790.0921.0000.1620.0560.0460.1650.237
K_Scatch0.5150.6220.1980.1980.2760.0930.0000.4610.7020.2440.5830.5270.4650.2670.3730.8190.3130.7150.8100.8290.5890.5550.2250.5600.4060.4060.2950.1450.1621.0000.0920.0790.2540.364
Stains0.1940.1690.0630.0630.0000.0000.0000.0160.2770.0950.1410.1930.2130.0000.1050.0600.2620.1270.8510.4120.6000.2530.3340.4210.1500.1500.2040.0480.0560.0921.0000.0110.0940.138
Dirtiness0.3290.3190.2560.2560.0000.0000.0000.0000.1270.0620.0730.2770.3380.0300.2800.0420.2610.0780.1270.1450.0510.3030.0840.0710.0760.0760.1060.0390.0460.0790.0111.0000.0800.119
Bumps0.2830.2560.1940.1940.0660.0000.0000.1170.2790.1080.2960.1640.2780.2580.2860.2170.2410.2250.2890.2470.3300.2860.1210.2510.3040.3040.1040.1480.1650.2540.0940.0801.0000.370
Other_Faults0.2070.2360.2060.2060.0900.0000.0000.1600.2580.1440.2370.3220.2050.0530.1650.2740.1580.2550.2990.3050.2100.2380.1180.1710.0000.0000.0210.2140.2370.3640.1380.1190.3701.000

Missing values

2023-05-18T20:08:14.290397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-18T20:08:14.847679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

X_MinimumX_MaximumY_MinimumY_MaximumPixels_AreasX_PerimeterY_PerimeterSum_of_LuminosityMinimum_of_LuminosityMaximum_of_LuminosityLength_of_ConveyerTypeOfSteel_A300TypeOfSteel_A400Steel_Plate_ThicknessEdges_IndexEmpty_IndexSquare_IndexOutside_X_IndexEdges_X_IndexEdges_Y_IndexOutside_Global_IndexLogOfAreasLog_X_IndexLog_Y_IndexOrientation_IndexLuminosity_IndexSigmoidOfAreasPastryZ_ScratchK_ScatchStainsDirtinessBumpsOther_Faults
0425027090027094426717442422076108168710800.04980.24150.18180.00470.47061.00001.02.42650.90311.64350.8182-0.29130.58221000000
16456512538079253810810810301139784123168710800.76470.37930.20690.00360.60000.96671.02.03340.77821.46240.7931-0.17560.29841000000
282983515539131553931718197972991251623101000.97100.34260.33330.00370.75000.94741.01.85130.77821.25530.6667-0.12280.21501000000
3853860369370369415176134518996991261353012900.72870.44130.15560.00520.53851.00001.02.24550.84511.65320.8444-0.15680.52121000000
412891306498078498335240960260246930371261353011850.06950.44860.06620.01260.28330.98851.03.38181.23052.40990.9338-0.19921.00001000000
543044110025010033763020876235764127138701400.62000.34170.12640.00790.55001.00001.02.79931.04141.93950.8736-0.22670.98741000000
641344613846813888390522304321481991231991687011500.48960.33900.07950.01960.14350.96071.03.95671.51852.61810.92050.27911.00001000000
71902002109362109561321120200071241721687011500.22530.34000.50000.00590.90911.00001.02.12061.00001.30100.50000.18410.33591000000
8330343429227429253264152629748531481687011500.39120.21890.50000.00770.86671.00001.02.42161.11391.41500.5000-0.11970.55931000000
97490779144779308150646167180215531431687011500.08770.42610.09760.00950.34780.98201.03.17781.20412.21480.9024-0.06511.00001000000
X_MinimumX_MaximumY_MinimumY_MaximumPixels_AreasX_PerimeterY_PerimeterSum_of_LuminosityMinimum_of_LuminosityMaximum_of_LuminosityLength_of_ConveyerTypeOfSteel_A300TypeOfSteel_A400Steel_Plate_ThicknessEdges_IndexEmpty_IndexSquare_IndexOutside_X_IndexEdges_X_IndexEdges_Y_IndexOutside_Global_IndexLogOfAreasLog_X_IndexLog_Y_IndexOrientation_IndexLuminosity_IndexSigmoidOfAreasPastryZ_ScratchK_ScatchStainsDirtinessBumpsOther_Faults
1931523567266325266337209673026833119141136001400.76910.60420.27270.03230.65670.40000.02.32011.64351.0792-0.72730.00300.81830000001
1932239269276029276047299512237820116140136001400.35150.44630.60000.02210.58820.81820.02.47571.47711.2553-0.4000-0.01180.82990000001
19333674222896472896653551165846882123143136001400.53970.64140.32730.04040.47410.31030.02.55021.74041.2553-0.67270.03170.98990000001
1934137170301492301511304592635778111126136001400.20150.51520.57580.02430.55930.73080.02.48291.51851.2787-0.4242-0.08050.89710000001
1935238287315114315142671913986424119143136001400.35000.51090.57140.03600.53850.71790.02.82671.69021.4472-0.42860.00620.99920000001
1936249277325780325796273542235033119141136001400.36620.39060.57140.02060.51850.72730.02.43621.44721.2041-0.42860.00260.72540000001
1937144175340581340598287442434599112133136001400.21180.45540.54840.02280.70460.70830.02.45791.49141.2305-0.4516-0.05820.81730000001
1938145174386779386794292402237572120140136001400.21320.32870.51720.02130.72500.68180.02.46541.46241.1761-0.48280.00520.70790000001
1939137170422497422528419974752715117140136001400.20150.59040.93940.02430.34020.65960.02.62221.51851.4914-0.0606-0.01710.99190000001
1940126112818795187967103262211682101133136010800.11620.67810.80000.01470.76920.72730.02.01281.30101.2041-0.2000-0.11390.52960000001